Robust Design Optimization and Emerging Technologies for Electrical Machines: Challenges and Open Problems
Source abstract
Abstract
The bio-inspired algorithms are novel, modern, and efficient tools for the design of electrical machines. However, from the mathematical point of view, these problems belong to the most general branch of non-linear optimization problems, where these tools cannot guarantee that a global minimum is found. The numerical cost and the accuracy of these algorithms depend on the initialization of their internal parameters, which may themselves be the subject of parameter tuning according to the application. In practice, these optimization problems are even more challenging, because engineers are looking for robust designs, which are not sensitive to the tolerances and the manufacturing uncertainties. These criteria further increase these computationally expensive problems due to the additional evaluations of the goal function. The goal of this paper is to give an overview of the widely used optimization techniques in electrical machinery and to summarize the challenges and open problems in the applications of the robust design optimization and the prospects in the case of the newly emerging technologies.
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Research context
OpenAlex topics: Advanced Multi-Objective Optimization Algorithms · Metaheuristic Optimization Algorithms Research · Evolutionary Algorithms and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Theory of Electrical Engineering, University of West Bohemia, Univerzitni 26, 306 14 Pilsen, Czech Republic
Department of Electrical Power Engineering and Mechatronics, Tallinn University of Technology, Tallinn 19086, Estonia
Faculty of Control Systems and Robotics, ITMO University, Saint Petersburg 197101, Russia
EDP Distribuicao, Direction of Market Platform, R. Camilo Castelo Branco 43-7th Floor, 1050-044 Lisbon, Portugal
Uninova-CTS, FCT Campus, 2829-516 Caparica, Portugal
Funding
Estonian Research Competency Council
References (100+)
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